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Article Dans Une Revue iScience Année : 2023

Identification of patient subtypes based on protein expression for prediction of heart failure after myocardial infarction

Résumé

This study investigates the ability of high-throughput aptamer-based platform to identify circulating biomarkers able to predict occurrence of heart failure (HF), in blood samples collected during hospitalization of patients suffering from a first myocardial infarction (MI). REVE-1 (derivation) and REVE-2 (validation) cohorts included respectively 254 and 238 patients, followed up respectively 9 · 2 ± 4 · 8 and 7 · 6 ± 3 · 0 years. A blood sample collected during hospitalization was used for quantifying 4,668 proteins. Fifty proteins were significantly associated with long-term occurrence of HF with all-cause death as the competing event. k-means, an unsupervised clustering method, identified two groups of patients based on expression levels of the 50 proteins. Group 2 was significantly associated with a higher risk of HF in both cohorts. These results showed that a subset of 50 selected proteins quantified during hospitalization of MI patients is able to stratify and predict the long-term occurrence of HF.
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Licence : CC BY - Paternité

Dates et versions

inserm-04191363 , version 1 (30-08-2023)

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Paternité

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Wilfried Heyse, Vincent Vandewalle, Guillemette Marot, Philippe Amouyel, Christophe Bauters, et al.. Identification of patient subtypes based on protein expression for prediction of heart failure after myocardial infarction. iScience, 2023, 26 (3), pp.106171. ⟨10.1016/j.isci.2023.106171⟩. ⟨inserm-04191363⟩
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